Synergizing Multigrid Algorithms with Vision Transformer: A Novel Approach to Enhance the Seismic Foundation Model

This study analyzes the contribution of artificial intelligence (AI) to the translation industry over 45 years, from 1980 to 2024. It examines 13,220 articles from sources like WoS, Scopus, and Lens, identifying 9,836 unique records for analysis. The research includes scientometric and thematic analyses, focusing on clusters, subject categories, keywords, and research centers. Additionally, it reviews 18 selected articles, highlighting trends such as machine translation, statistical machine translation, and low-resource languages, emphasizing their significance for future directions in the tra…

Recent research highlights the potential of task arithmetic for editing pre-trained closed-vocabulary models, particularly in image classification. This study investigates task addition in closed-vocabulary models, revealing that weight disentanglement is a common outcome of pre-training. The findings suggest that closed-vocabulary vision transformers can be effectively modified using task arithmetic, leading to enhanced multi-task model deployment capabilities.

The article discusses the integration of Artificial Intelligence (AI) into communication networks, highlighting the increased energy consumption associated with this shift. It presents a new metric called the Energy Cost of AI Lifecycle (eCAL), which quantifies the energy used during the development, deployment, and utilization of AI models in communication systems. The study emphasizes the need for a comprehensive understanding of energy consumption metrics, which traditionally focus on communication, computation infrastructure, or model development.

The paper titled 'LampQ: Towards Accurate Layer-wise Mixed Precision Quantization for Vision Transformers' presents a new method for quantizing pre-trained Vision Transformer models. The proposed Layer-wise Mixed Precision Quantization (LampQ) addresses limitations in existing quantization methods, such as coarse granularity and metric scale mismatches. By employing a type-aware Fisher-based metric, LampQ aims to enhance both the efficiency and accuracy of quantization in various tasks, including image classification and object detection.

Artificial intelligence (AI) in media has seen rapid advancements over the past decade, particularly with the introduction of Generative Adversarial Networks (GANs) and diffusion models, which have enhanced photorealistic image generation. However, these developments have also led to challenges in distinguishing between real and synthetic content, as evidenced by the rise of deepfakes. Many detection models utilizing deep learning methods like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have been created, but they often struggle with generalization and multimodal data.

Image deblurring is a crucial aspect of computer vision, focused on restoring sharp images from blurry ones caused by motion or camera shake. Traditional deep learning methods, including CNNs and Vision Transformers (ViTs), face challenges with complex blurs and high computational demands. A new dual-domain architecture integrates Vision Transformers with a frequency-domain FFT-ReLU module, enhancing the ability to suppress blur artifacts while preserving details, achieving superior performance metrics such as PSNR and SSIM in extensive experiments.

Recent advancements in large language models (LLMs) have demonstrated significant potential to enhance drug discovery processes. However, the specialized nature of biochemical data often requires expensive domain-specific fine-tuning, which poses challenges for the application of general-purpose LLMs. To overcome these obstacles, the proposed CLADD system utilizes retrieval-augmented generation (RAG) to facilitate dynamic information retrieval from biomedical knowledge bases, thereby improving the efficiency and effectiveness of drug discovery tasks.